Participant and Musical Diversity in Music Psychology Research
Bibliographic record
Abstract
Research on music psychology has increased exponentially over the past half century, providing insights on a wide range of topics underpinning the perception, cognition, and production of music. This wealth of research means we are now in a place to develop specific, testable theories on the psychology of music, with the potential to impact our wider understanding of human biology, culture, and communication. However, the development of more widely applicable and inclusive theories of human responses to music requires these theories to be informed by data that is representative of the global human population and its diverse range of music-making practices. The goal of the present paper is to survey the current state of the field of music psychology in terms of the participant samples and musical samples used. We reviewed and coded relevant details from all articles published in Music Perception, Musicae Scientiae, and Psychology of Music between 2010 to 2022. We found that music psychologists show a substantial tendency to collect data from young adults and university students in Western countries in response to Western music, replicating trends seen across psychology research as a whole. Even data collected in non-Western countries tends to come from a similar demographic to studies of Western participants (e.g., university students, young adults). Some positive trends toward increasing participant diversity have been evidenced over the past decade, although there is still much work to be done, and certain subtopics in the field appear to be more prone to these sampling biases than others. We discuss recent methodological developments in the field that promote further diversification of our research and highlight subsequent changes that will be needed at group or institutional levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.188 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".